Rather than employing a carefully designed task to learn feature representations [1]}, [2]}, CL tries to maximize the agreement between differently augmented views of the same image. Data augmentation, a widely used technique in deep learning, is applied to generate different views of a sample in CL. Some data augmentation operations that are commonly used in the natural image field may destroy important diagnostic features when transferred to the fundus image field. Therefore, as shown in part B of Fig. REF , four augmentation operations are considered in our work: cropping, color distortion, gray scaling, and rotation.
